Here are some of today’s Technologies and Services that use Deep Learning, Data Science, and AI.
Deep Learning:
Deep learning is machine learning’s most powerful technique for making the future happen. Much like the neurons in our brains, deep learning is the connection or the powerhouse present between data science and AI. Both machine learning and its sub-type, deep learning, incorporate the process of learning from the data over time. While it is not the only thing connecting the two, deep learning is a type of machine learning that works best to strengthen the process of AI and data science. Deep learning can simply be defined as a machine learning technique that endeavors to teach computer systems things that come naturally to humans.
For example, we can naturally interpret what a stop sign on the road means, but for a machine to locate this sign and interpret it, it needs a lot of learning and implementation. This stage of learning is called deep learning. Once we understand the concepts behind deep learning, we'll understand that it is indeed deep learning that shapes the reality behind driverless cars and voice control that we have become accustomed to.
Machine Learning tools and techniques are the two key narrow subsets. That only focuses more on deep learning. Furthermore, we need to apply it to solve any problem. That requires thought- human or artificial.
Any Deep neural network will consist of three types of layers:
Machine Learning tools and techniques are the two key narrow subsets. That only focuses more on deep learning. Furthermore, we need to apply it to solve any problem. That requires thought- human or artificial.
Any Deep neural network will consist of three types of layers:
- The Input Layer
- The Hidden Layer
- The Output Layer
Example of Deep Learning:
Say you are playing a game along with four of your friends. Your friends are the different layers of the artificial neural network - all standing in a row one behind the other. There is a moderator to enforce the rules of the game. A picture is shown to the first person. His objective is to extract as much information as he can and pass it on along the row. The game shall go on until the last person can describe the image accurately.
While playing, all members standing in the queue will have to alter the information, little at a time, to improve the eventual description. Deep learning works in a similar fashion. It refines the information it collects over time to deliver finer results.
Artificial Intelligence:
The term “AI” is used so often nowadays that we have a basic understanding of what it means: a computer’s ability to perform tasks such as visual perception, speech recognition, decision-making, and language translation. AI has progressed rapidly over the last few years, but it is still nowhere near matching the vast dimensions of human intelligence.
Humans make quick use of all the data around them and can use what they have stored in their minds to make decisions. However, AI does not yet boast such abilities, instead, it using huge chunks of data to clear its objectives. This ultimately means that AI might require huge chunks of data for doing something as simple as editing text.
Example of Artificial Intelligence:Humans make quick use of all the data around them and can use what they have stored in their minds to make decisions. However, AI does not yet boast such abilities, instead, it using huge chunks of data to clear its objectives. This ultimately means that AI might require huge chunks of data for doing something as simple as editing text.
Some machines aren't getting smarter in the existential sense, but they are improving their skills and usefulness based on a large dataset.
These are some of the most popular examples of artificial intelligence that's being used today
- Siri
- Tesla
- Alexa
- Cogito
- John Paul
- Boxever
- Amazon.com
- Netflix
- Pandora
- Nest
Data Science:
Data science is much more than just simple machine learning. Data here may not have been obtained through a machine, and it may not even be for learning purposes. Simply put, data science tends to cover the whole spectrum of data processing as we know it. Data science is not just related to the statistical aspect of the process, but it feeds the process and derives benefits out of it through data engineering. Data engineers and data scientists have a huge role to play in propelling AI forward.
Example of Data Science:
Let's say you are crazy about Cricket, which I am sure you are, and there is an ongoing series between India and Australia. India loses the first two matches, much to your disappointment, and you are eager to know what will happen in the next game. You go online to check the results of past encounters between the two nations and notice a trend -
every time India has lost two games in a row against Australia, they have come back strongly in the third. You are convinced India will win the next game, and predict the outcome. To everyone's surprise, your prediction turns out right. Congratulations, you're a data scientist!
The
numbers and statistics that a data scientist observes in the real world may not be so simple, and he/she might even require software to recognize the underlying trends. Nonetheless, the basic idea is the same. Due to its efficiency in predicting outcomes, data science is useful in developing artificial intelligence - what we will explore next.
Applications of 3 Giant Industries- Deep Learning, AI and Data Science:
The use of deep learning, data science, and AI in tandem has opened the door for myriad opportunities. AI has a major role to play in shaping the benefits that we may enjoy in the future.
Here are some of today’s technologies and services that use Deep Learning, Data Science, and AI.
Here are some of today’s technologies and services that use Deep Learning, Data Science, and AI.
- Google:
Google is pleased to have made use of enhanced deep learning and data science algorithms that make sure to provide users with content deemed relevant for them.
The search engine uses machine learning algorithms to find out a plethora of data regarding what people are searching and combs through more than a billion pages to rank the ones that are best for you first. All of this is done within the matter of microseconds. Amazing, right?
- Robotics:
A lettuce production company by the name of Spread has revealed their plans to equip robots for handling affairs within the farms. By harvesting 30,000 lettuce heads every day, robots will drastically increase efficiency. The processors within these robots have been fed a high plethora of data regarding the process it takes to harvest lettuce. Not only will this AI revolution increase efficiency, but it will also open doors to new possibilities.
- Speech Recognition:
Thanks to the use of AI and numerous endeavors by smartphone manufacturers, you can ask speech recognition software to locate the nearest ice-cream shop or order pizza, without typing a word. It is the creation of artificial neural networks that enforces the understanding computers have of what you say. It takes exhaustive machine learning to do this through AI.
- Expert Systems:
Watson by IBM is a perfect example of how expert systems can benefit from the collaboration between deep learning, data science, and AI. The computer, which is powered by AI can collect, absorb, and process data much quicker than humans. Watson can not only display a solution really quickly, but it can also diagnose cancer with an unbelievable accuracy of 90 percent due to its vast knowledge, whereas well-trained doctors know only around 20 percent of the updates present in the diagnosis.
Conclusion:
Artificial intelligence is a computer program that is capable of being smart. It can mimic human thinking or behavior. Machine learning falls under artificial intelligence. That is to say, all machine learning is artificial intelligence, but not all artificial intelligence is machine learning. For example, a simple scheduling system may be artificial intelligence, but not necessarily a machine that can learn. Then we have deep learning, which is, again, a subset of machine learning. Both - machine learning and deep learning - fall under the broad category of artificial intelligence. Lastly, data science is a very general term for processes that extract knowledge or insights from data in its various forms. Although it has no direct relation to artificial intelligence, machine learning or deep learning, it can be useful to each of the three.
We have briefly studied Data Science vs. Artificial Intelligence vs. Deep Learning. We also learned clearly what every language is specified for. If you have any questions, feel free to ask in the Message section.